(1) Field of Invention
The present invention relates to a system for object recognition and, more particularly, to a system for object recognition on degraded imagery.
(2) Description of Related Art
The prior art for object detection and recognition has been dominated by approaches that extract features, such as edges or gradients, from an image without taking into account how the image was generated through three-dimensional-two-dimensional (3D-2D) projection. For example, many current methods for object detection are based on the Haar Feature-based Cascade Classifiers (see List of incorporated Literature References, Literature Reference Nos. 1 and 2), Histograms of Oriented Histograms (see Literature Reference No. 3), or Deformable Parts Model (see Literature Reference No. 4). Such approaches are sensitive to structural noise that deviates from Gaussian noise.
Deep learning, particularly, convolutional neural networks (see Literature Reference Nos. 6 and 7) have shown the best performance for object recognition. However, these techniques can also fail in the presence of structural noise.
Thus, a continuing need exists for a method of object recognition that uses information regarding ego-motion of the camera and the relative velocity of the surroundings and objects to estimate the expected transformation in the camera image to avoid the effects of structural noise.